上海农业学报2023，Vol.39Issue(6)：109-117,9.DOI:10.15955/j.issn1000-3924.2023.06.20
基于YOLOv4的稻田杂草目标检测算法
Weed target detection algorithm in paddy field based on YOLOv4
摘要
Abstract
Aiming at the problem of accurate and rapid identification of weeds in paddy field during the operation of intelligent weed control equipment in precision agriculture,a method of weed detection in paddy field based on YOLOv4 algorithm is proposed.According to PASCAL VOC data set format,a paddy field weed target detection data set was constructed.Depth separable convolution was used to replace the original standard convolution,and inverted residual unit was used to replace the residual unit in CSP-Darknet to reduce the number of parameters and improve the detection speed of the model.In addition,the boundary box size obtained by K-means clustering algorithm was applied to each scale network layer,and the Generative Adversarial Network(GAN)noise layer was added to the adaptive feature pool output of the Path Aggregation Network(PANet)to improve the detection accuracy of the model.The improved model was trained on GPU server and compared its performance with the original YOLOv4 algorithm through experiments.The results showed that the mean Average Precision(mAP)of the improved algorithm was 4％higher than that of original algorithm on the test set,reaching 97％;The detection speed had increased by 12.1 frames/s,reaching 60.3 frames/s,with significant improvement effects.It had the advantages of good real-time performance,high accuracy,and strong robustness,which could better achieve the detection of weeds in rice fields by intelligent weed control equipment,greatly saving manpower and material resources.关键词
杂草识别/目标检测/深度学习/YOLOv4Key words
Weed recognition/Target detection/Deep learning/YOLOv4分类
农业科学引用本文复制引用
袁涛,胡冬,马超,李琳一,郑秀国,钱戴玲..基于YOLOv4的稻田杂草目标检测算法[J].上海农业学报,2023,39(6):109-117,9.基金项目
上海市科技兴农技术创新项目(2022-02-08-00-12-F01183) （2022-02-08-00-12-F01183）
上海市农业科学院卓越团队建设项目(沪农科卓[2022]015) （沪农科卓[2022]015）
